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RATIONAL DRUG DESIGN
Drug Design & Discovery: Introduction
Drugs:
Natural sources
Synthetic sources
Targets:
Ideal Drug
1) target: bio-molecule ,involved in signaling
or metabolic pathways, that are specific to
disease process by either protein-protein or
protein-nucleic
protein
nucleic acid interactions.
interactions
2)antagonist action-inhibiting functions of the
disease causing proteins.
3) Inhibiting interactions of the proteins.
4)Activates other proteins, that are deregulated
in such disease like cancer.
Discovering and Developing the
‘One Drug’
Administrative Support Analytical Chemistry Animal Health Anti-infective Disease Bacteriology
Over 100
Different
Disciplines
Working Together
Behavioral Sciences Biochemistry Biology Biometrics Cardiology Cardiovascular Science Clinical Research
Communication Computer Science Cytogenetics Developmental Planning DNA Sequencing Diabetology
Document Preparation Dosage Form Development Drug Absorption Drug Degradation Drug Delivery
Electrical Engineering Electron Microscopy Electrophysiology Environmental Health & Safety
Endocrinology Enzymology Facilities Maintenance Fermentation Finance
Employee Resources
Formulation
Gastroenterology Graphic Design Histomorphology Intestinal Permeability Law Library Science
Medical Services
Mechanical Engineering Medicinal Chemistry Molecular Biology Molecular Genetics Molecular Models
Natural Products Neurobiology Neurochemistry Neurology Neurophysiology Obesity
Oncology Organic Chemistry Pathology Peptide Chemistry Pharmacokinetics Pharmacology Photochemistry
Physical Chemistry Physiology Phytochemistry Planning Powder Flow Process Development
Project Management Protein Chemistry Psychiatry Public Relations Pulmonary Physiology
Radiochemistry Radiology Robotics Spectroscopy Statistics Sterile Manufacturing Tabletting Taxonomy
Technical Information Toxicology Transdermal Drug Delivery Veterinary Science Virology X-ray Spectroscopy
Drug Design
- Molecular Modeling
- Virtual Screening
Identify disease
Isolate protein
involved in
disease (2-5 years)
Find a drug effective
against disease protein
(2-5 years)
Scale-up
Preclinical testing
(1-3 years)
Human clinical trials
(2-10 years)
Formulation
FDA approval
(2-3 years)
1)challenging
2)Expensive
3)Time consuming
So, Multidisciplinary approach:
Computational tools, methodologies for structure guided approach + Global gen
expression data analysis by softwares.
Hence,
1)
Efficiency increased
2)
Cost effectiveness
3)
Time saved
4)
Strategies to overcome toxic side effects
Medicinal chemists today are
facing a serious
challenge because of the increased cost and enormous
amount of time taken to discover a new drug, and also
because of fierce competition amongst different drug
companies .
GENOMICS, PROTEOMICS & BIOPHARM.
Potentially producing many more targets
and “personalized” targets
HIGH THROUGHPUT SCREENING
Identify disease
Screening up to 100,000 compounds a
day for activity against a target protein
VIRTUAL SCREENING
Using a computer to
predict activity
Isolate protein
COMBINATORIAL CHEMISTRY
Rapidly producing vast numbers
of compounds
Find drug
MOLECULAR MODELING
Computer graphics & models help improve activity
IN VITRO & IN SILICO ADME MODELS
Tissue and computer models begin to replace animal testing
Preclinical testing
Drug Discovery overview
Approaches to drug discovery:
•Serendipity (luck)
•Chemical
C e ca Modification
od cat o
•Screening
•Rational
ways:
A) Development of ligands with desired properties for
targets having known structure and functions.
B)
Development of ligands with predefined properties for
targets whose structural information may be or may not
be known.
This, unknown target information can be found by global
gene expression data.
In 1970s the medicinal chemists considered molecules
as topological entities in 2 dimension (2D) with
associated chemical properties.
QSAR concept became quite popular. It was
implemented in computers and constituted first
generation rational approach to drug design
The acceptance by medicinal chemists of molecular
modeling was favored by the fact that the QSAR was
now supplemented by 3D visualization.
The “lock and key” complementarily is actually
supported
t db
by 3D model.
d l Computer
C
t aided
id d molecular
l l
design (CAMD) is expected to contribute to intelligent
lead .
Ancient times: Natural products with biological activities
used as drugs.
Chemical Era: Synthetic organic compounds
Rationalizing design process: SAR & Computational
Chemistry based Drugs
Biochemical era: To elucidate biochemical pathways and
macromolecular structures as target as well as drug.
NMR and X-ray
structure determination
QM, MM methods
QSAR/3D QSAR
Structure-based drug design
Rational drug design
Model construction
Molecular mechanics
Conformational searches
Molecular dynamics
Combinatorial chemistry
Chemical similarity
Chemical diversity
Homology modeling
Bioinformatics
Chemoinformatics
Molecular Graphics: Visual representation
of molecules & their properties.
Computational Chemistry:
Chemistry: Simulation of
atomic/molecular properties of compound
through computer solvable equations.
( b’b’-b’0)[ V1cos ] b’
( - 0) [V1cos ]
Statistical Modeling:
Modeling: D-R, QSAR/3
QSAR/3-D QSAR Molecular data
Information Management:
Management: Organizational databases retrieval
/search & processing of properties of 1000
1000…
… of compounds.
MM = Computation + Visualization + Statistical modeling
+ Molecular Data Management
(A) MOLECULAR MECHANICS (MM)
(B) QUANTUM MECHANICS (QM)
Quantum Mechanics (QM)
Ab-initio and semi
Absemi--empirical methods
Considers electronic effect & electronic
structure of the molecule
Calculates charge distribution and orbital
energies
Can simulate bond breaking and formation
Upper atom limit of about
atoms
Molecular Mechanics (MM)
Totally empirical technique applicable to
both small and macromolecular systems
a molecule is described as a series of
charged points (atoms) linked by springs
(bonds)
The potential energy of molecule is
described by a mathematical function called
a FORCE FIELD
When Newton meets Schrödinger...
Sir Isaac Newton
(1642 - 1727)
F = ma
Erwin Schrödinger
(1887 - 1961)
Ĥ
=
When Quantum Chemistry Starts to Move...
Traditional QC
Methods
Mixed
QuantumClassical
First-Principles
Car-Parrinello
MD
Classical MD
Simulations
Mixed Quantum-Classical
in a complex environment - QM/MM
Main idea
Partitioning the system into
1. chemical active part
t t d by
treated
b QM methods
th d
2. Interface region
3. large environment that is
modeled by a classical
force field
Classical MM
interface
QM
Mixed Quantum-Classical
in a complex environment - QM/MM
Main idea
Partitioning the system into
1. chemical active part
t t d by
treated
b QM methods
th d
2. Interface region
3. large environment that is
modeled by a classical
force field
Classical MM
interface
QM
Receptor Structure
Unknown
Ligand
Li
d
Structure
Known
Unknown
Known
Generate 3D structures,
Similarity/dissimilarity
Homology modelling
HTS, Comb.
Comb Chemistry
Active Site Search
Receptor Based DD
de NOVO design,
3D searching
(Build the lock, then find the key)
(Build or find the key that fits the lock)
Indirect DD
Ligand-Based DD
Analogs Design
Rational Drug Design
(Structure-based DD)
Molecular Docking
(Drug-Receptor
interaction)
2D/3D QSAR &
Pharmacophore
Computer Aided Drug Design Techniques
- Physicochemical Properties Calculations
- Partition Coefficient (LogP), Dissociation Constant (pKa) etc.
- Drug Design
- Ligand Based Drug Design
- QSARs
- Pharmacophore Perception
- Structure
St t
Based
B d Drug
D
Design
D i
- Docking & Scoring
- de-novo drug design
- Pharmacokinetic Modeling (QSPRs)
- Absorption, Metabolism, Distribution and Toxicity etc.
- Cheminformatics
- Database Management
- Similarity / Diversity Searches
-All techniques joins together to form VIRTUAL SCREENING protocols
QSARs are the mathematical relationships linking chemical structures with
biological activity using physicochemical or any other derived property as an
interface.
Biological Activity = f (Physico
Physico--chemical properties)
Mathematical Methods used in QSAR includes various regression and
pattern recognition techniques.
Physicochemical or any other property used for generating QSARs is termed
as Descriptors and treated as independent variable.
Biological property is treated as dependent variable.
Compounds + biological activity
QSAR
New compounds with
improved biological activity
Receptor-based Drug Design
•Examine the 3D structure of the biological target (an X-ray/ NMR
structure.
•Hopefully one where the target is complexed with a small molecule
ligand (Co-crystallized)
•Look for specific chemical groups that could be part of an attractive
interaction between the target protein and the ligand.
•Design
Design a new ligands that will have sites of complementary
interactions with the biological target.
Advantage: Visualization allows
direct design of molecules
Put a compound in the approximate area where
binding occurs
Docking algorithm encodes orientation of
compound and conformations.
Optimize binding to protein
Minimize energy
Hydrogen bonding
Hydrophobic interactions
Scoring
• Can pursue both receptor and pharmacophore-based approaches
independently
• If the binding mode of the ligand and target is known,
information from each approach can be used to help the other
Ideally, identify a structural model that explains the biological
activities of the known small molecules on the basis of their
interactions with the 3D structure of the target protein.
Typical projects are not purely receptor or pharmacophore-based;
theyy use combination of information, hopefully
p
y synergistically
y g
y
Drug Design Successes (Fruits of QSAR)
Name of the drug discovered
1. Erythromycin analogs
2. New Sulfonamide dervs
dervs..
3. Rifampicin dervs.
dervs.
4. Napthoquinones
5. Mitomycins
6. Pyridine –2-methanol’s
7. Cyclopropalamines
8. -Carbolines
9. Phenyl oxazolidines
10..Hydantoin dervs.
10
dervs.
11..Quinolones
11
Biol. Activity
Biol.
Antibacterial
Antibacterial
Anti--T.B.
Anti
Antimalerials
Antileukemia
Spasmolytics
MAO inhibitors
MAO Inhibitors
Radioprotectives
Anti CNS
CNS--tumors
Antibacterial
While we are still waiting for a drug totally designed
from scratch, many drugs have been developed with
major contributions from computational methods
O
O
NH
CO2H
F
N
MeO
N
Et
HN
HO
donepezil (1996)
Alzheimer's treatment
acetylcholinesterase inhibitor
shape analysis and docking studies
N NH
N
N
N
N
S
O2
MeO
norfloxacin (1983)
antibiotic
first of the 6-fluoroquinolones
QSAR studies
Cl
N
Bu
losartan [Cozaar] (1995)
angiotensin II antagonist
anti-hypertensive
Modeling Angiotensin II octapeptide
O
O
NH
H
S
SO2NH2
dorzolamide [Trusopt] (1994)
glaucoma treatment
carbonic anhydrase inhibitor
SBLD and ab initio calcs
H
N
NMe2
zolmatriptan [Zomig] 1995
5-HT1D agonist
migraine treatment
Molecular modeling
HIV-1 protease inhibitors
N
Ph
H
N
OH
N
N
N
H
N
O
O
S
indinavir [Crixivan] (Merck, 1996)
X-rayy data from enzyme
y
and molecular mechanics
Me
HO
SPh O
O
N
H
N
Me
H
N
N
H
O
Ph
H
nelfinivir [Viracept] (Agouron, 1996)
OH
O
N
H
S
O
N
ritonavir [Norvir] (Abbott, 1995)
peptidomimetic
p
p
strategy
gy
H
N
H
OH
Ph
O
OH
H
N
N
O
O
Ph
N
H
OH
CONH2
O
H
N
H
H
saquinavir [Invirase, Fortovase] (Roche, 1990)
transition state mimic of enzyme substrate
Drug Discovery is a multidisciplinary, complex, costly and
intellect intensive process.
Modern drug design techniques can make drug discovery
process more fruitful & rational.
Knowledge management and technique specific expertise can
save time & cost, which is a paramount need of the hour.